The present invention is related to computer-aided analysis of lung nodules in medical images. In particular, the invention is directed to computer-aided review of computed tomography images for initially detecting lung nodule candidates for a subsequent analysis based upon the features of the lung nodule candidates.
Lung cancer is the leading cause of cancer deaths among the population in-the United States. Each year there are about 170,000 newly diagnosed cases of lung cancer and over 150,000 deaths. More people die of lung cancer than of colon, breast, and prostate cancers combined. Despite the research and improvements in medical treatments related to surgery, radiation therapy, and chemotherapy, currently the overall survival rate of all lung cancer patients is only about 14 percent. Unfortunately the survival rate has remained essentially the same over the past three decades. The high mortality rate of lung cancer is caused by the fact that more than 80% lung cancer is diagnosed after it has metastasized. Patients with early detection of lung cancer followed by proper treatment with surgery and/or combined with radiation and chemotherapy can improve their five-year survival rate from 13 percent to about 41 percent. Given that earlier-stage intervention leads to substantially higher rates of survival, it is therefore a major public health directive to reduce the mortality of lung cancer through detection and intervention of the cancer at earlier and more curable stages.
The development of the computed tomography (CT) technology and post-processing algorithms has provided radiologists with a useful tool for diagnosing lung cancers at earlier stages. However, current CT systems have their inherent shortcomings in that the amount of chest CT images (data) that is generated from a single CT examination, which can range from 30 to over 300 slices depending on image resolution along the scan axial direction, becomes a huge hurdle for the radiologists to interpret. Even though small lung nodules can be captured by helical CT and the images can be viewed in either a traditional film-based mode or cine mode on today""s Picture Archiving and Communication System (PACS) workstations, the potential of overlooking small nodules in the diagnostic process has become major concerns.
Accordingly there is a need for a system that automatically identifies small lung nodule candidates from helical CT images to assist radiologists in improving the detection of nodules in the clinical practice.
The present invention is a method and apparatus for analyzing volumetric chest computed tomography images for lung nodules. The steps of the method of the invention include initially obtaining the volumetric chest computed tomography images for lung nodules from an image acquisition device. The lungs are then separated from the other anatomic structures on the images to form lung images. The lung images are then processed to detect nodule candidates with a local density maximum algorithm. False-positives among the detected nodule candidates are then reduced by an application of parameters concerning lung nodules. Preferably the parameters concern at least the size and shape of the nodule candidates.
The present invention similarly includes an article of manufacture for analyzing volumetric chest computed tomography images for lung nodules. The article includes a machine readable medium containing one or more programs which when executed implement the method of the invention.
For a better understanding of the present invention, reference is made to the following description to be taken in conjunction with the accompanying drawings and its scope will be pointed out in the appended claims.